Correcting for defects in a digital image taken by an image sensor caused by pre-existing defects in two pixels in adjacent columns of an image sensor

ABSTRACT

A method for correcting for defects in a digital image taken by an image sensor when there are pre-existing defects in two pixels in adjacent columns of the image sensor which causes two adjacent lines of pixels in the digital image to have corrupted data.

CROSS REFERENCE TO RELATED APPLICATION

Reference is made to commonly-assigned U.S. patent application Ser. No.09/788,798 filed concurrently herewith, entitled “Correcting Defects InA Digital Image Caused ByA Pre-Existing Defect In A Pixel Of An ImageSensor” by John F. Hamilton, Jr., the disclosure of which isincorporated herein.

FIELD OF THE INVENTION

The present invention relates to correcting for corrupted data in adigital image caused by defective pixels in an image sensor.

BACKGROUND OF THE INVENTION

In certain types of image sensors, when there is a defect in two pixelsof such sensor it causes two adjacent lines of pixels in a digital imageto have corrupt data. This happens during the transfer of electronscorresponding to pixels when such electrons are transferred through thedefective pixel. An example of this situation is a full frame imagesensor. In a typical full frame image sensor after an image is captured,electrons stored in the pixels of such sensor are transferred a line ata time through the pixels of the image sensor. A defective pixel willcorrupt data stored in the electrons of subsequent pixels which passthrought it. This causes a line of corrupted pixel data. In a full frameimage sensor, a column defect is an anomaly in the structure of an imagesensor that prevents the vertical transfer of pixel charge packets. As aconsequence, none of the affected pixels in the adjacent columns ofdefective pixels can provide valid image information. If left untreated,this condition would produce a partial height or a full height adjacentvertical lines of artifacts running through the image. The currentmethod of concealing a column defect is to average nearest horizontalneighbors of the same filter type. In a standard color filter array(CFA), for example, the Bayer CFA pattern shown in commonly-assignedU.S. Pat. No. 3,971,065, that means averaging the pixel two positions tothe left with the pixel two positions to the right. While this methodworks well enough for the vast majority of pixels, it fails to properlyhandle corrupted pixels in certain image contexts, such as high contrastdiagonal edges. In addition, when the current method fails, it doesn'tfail gracefully, but rather with opposing vertical spikes of spuriouscolor.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an improved methodfor correcting for two adjacent lines of corrupted data in a digitalimage formed by an image sensor with defective pixels.

It is another object of the present invention to provide a method whichis particularly suited for correcting for adjacent column defects in afull frame image sensor and that works effectively for a variety ofscene content including high contrast diagonal edges.

These objects are achieved in a method for correcting for defects in adigital image taken by an image sensor when there are pre-existingdefects in two pixels in adjacent columns of the image sensor whichcauses two adjacent lines of pixels in the digital image to havecorrupted data, comprising the steps of:

(a) providing a defect map which identifies the position of thedefective pixels and specifies the two adjacent lines of pixels whichduring readout will be caused to have corrupted data;

(b) capturing the digital image in the image sensor and reading out suchdigital image to provide the digital image with the two adjacent linesof pixels in the digital image having corrupted data;

(c) computing classifiers based on adjacent non-corrupted pixel datawhich indicate that there is a horizontal edge or a diagonal edgefeature which passes through the defective lines of pixels; and

(d) adaptively replacing the data in the corrupted image pixels byselecting an algorithm which correponds to the edge feature identifiedby the classifier and using the valid data in the neighboringnon-corrupted pixels of the selected edge feature.

It is an advantage of the present invention to provide a concealmentalgorithm for correcting for corruption in two adjacent lines of pixeldata caused by defective pixels in an image sensor such as a full frameimage sensor. This algorithm significantly improves the efficacy ofcorrecting for a line of corrupted pixel data over a wide range of scenecontent.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an electronic still camera employing thedefect correction algorithm according to the present invention;

FIG. 2 is a diagram of green pixels around a corrupted green pixel in acorrupted column;

FIG. 3 is a diagram of green pixels around a corrupted green pixel inone of two adjacent corrupted columns;

FIG. 4 is a diagram of red, green, and blue pixels around a corruptedred pixel in a corrupted column; and

FIG. 5 is a diagram of red, green, and blue pixels around a corruptedred pixel in one of two adjacent corrupted columns.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Since single-sensor cameras employing color filter arrays are wellknown, the present description will be directed in particular toelements forming part of, or cooperating more directly with, apparatusand method in accordance with the present invention. Elements notspecifically shown or described herein may be selected from those knownin the art.

The present invention corrects for corrupted data in an output imagecaused by one or more defective pixels. Throughout the specification theterms “column” and “line” are used interchangeably. For example, a lineof pixels of corrupted data could also be referred to as a column ofcorrupted data. Moreover, when referenced is made to a column or line ofcorrupted data as will become clearer hereinafter, the entire column orline or a portion thereof or part of a line of column of an output dataimage may be corrupted. In such a case the corrupted portion will bereferred to a column or line of data.

Referring initially to FIG. 1, an electronic still camera is generallydivided into an input section 2 and an interpolating and recordingsection 4. The input section 2 includes an exposure section 10 fordirecting image light from a subject (not shown) toward an image sensor12. Although not shown, the exposure section 10 includes conventionaloptics for directing the image light through a diaphragm, whichregulates the optical aperature, and a shutter, which regulates exposuretime. The image sensor 12, which includes a two-dimensional array ofphotosites corresponding to picture elements (pixels) of the image, is aconventional charge-couple device (CCD) using well-known interlinetransfer or full frame transfer techniques. The image sensor 12 iscovered by a color filter array (CFA) 13, known as a Bayer array(commonly-assigned U.S. Pat. No. 3,971,065), in which each pixel in thesensor is covered by a colored filter. In particular, chrominance colors(red and blue) are interspersed among a checkerboard pattern ofluminance colors (green). The image sensor 12 is exposed to light sothat analog image charge information is generated in respectivephotosites. The charge information is applied to an output diode 14,which converts the charge information to analog image signalscorresponding to respective picture elements. The analog image signalsare applied to an A/D converter 16, which generates a digital imagesignal from the analog input signal for each picture element. Thedigital signals are applied to an image buffer 18, which may be a randomaccess memory (RAM) with storage capacity for a plurality of stillimages.

A control processor 20 generally controls the input section 2 of thecamera by initiating and controlling exposure (by opening the diaphragmand shutter (not shown) in the exposure section 10), by generating thehorizontal and vertical clocks needed for driving the image sensor 12and for clocking image information therefrom, and by enabling the A/Dconverter 16 in conjunction with the image buffer 18 for each signalsegnebt relating to the picture element. (The control processor 20 wouldordinarily include a microprocessor coupled with a system timingcircuit.) Once a certain number of digital image signals have beenaccumulated in the image buffer 18, the stored signals are applied to adigital signal processor 22, which controls the throughput processingrate for the interpolation and recording section 4 of the camera. Thedigital signal processor 22 applies an interpolation algorithm to thedigital image signals, and sends the interpolation signals to aconventional, removable memory card 24 via a connector 26.

Since the interpolation and related processing ordinarily occurs overseveral steps, the intermediate products of the processing algorithm arestored in a processing buffer 28. (The processing buffer 28 may also beconfigured as a part of the memory space of the image buffer 18.) Thenumber of image signals needed in the image buffer 18 before digitalprocessing can begin depends on the type of processing, that is, for aneighborhood interpolation to begin, a block of signals including atleast a portion of the image signals including a video frame must beavailable. Consequently, in most circumstances, the intepolation maycommence as soon as the requisite block of picture elements is presentin buffer 18.

The input section 2 operates at a rate commensurate with normaloperation of the camera while interpolation, which may consume moretime, can be relatively divorced from the input rate. The exposuresection 10 exposes the image sensor 12 to image light for a period oftime dependent upon exposure requirements, for example, a time periodbetween 1/1000 and several seconds. The image charge is then swept fromthe photosites in the image sensor 12, converted to a digital format,and written into the image buffer 18. The driving signals provided bythe control processor 20 to the image sensor 12, the A/D converter 16and the buffer 18 are accordingly generated to achieve such a transfer.The processing throughput of the interpolation and recording section 4is determined by the speed of the digital signal processor 22.

One desirable consequence of this achitecture is that the processingalgorithm employed in the interpolation and recording section may beselected for quality treatment of images rather than for throughputspeed. This, of course, can put a delay between consecutive pictureswhich may affect the user, depending on the time between photographicevents. This is a problem since it is well known and understood in thefield of electronic imaging that a digital still camera should provide acontinuous shooting capability for a successive sequence of images. Forthis reason, the buffer 18 shown in FIG. 1 provides for storage of aplurality of images, in effect permiting a series of images tp “stackup” at video rates The size of the buffer is established to hold enoughconsecutive images to cover most picture-taking situations.

An operational display panel 30 is connected to the control processor 20for displaying information useful in the operation of the camera. Suchinformation might include typical photographic data, such as shutterspeed, aperature, exposure bias, color balance (auto, tungsten,fluorescent, daylight), field-frame, low battery, low light, exposuremode (aperature preferred, shutter preferred), and so on. Moreover, otheinformation unique to this type of camera is displayed. For instance,the removable memory card 24 would ordinarily include a directorysignifying the beginning and ending of each stored image. This wouldshow on the display panel 30 as either (or both0 the number of imagesstored or the number of image spaces remaining, or estimated to beremaining.

Referring to FIG. 1, the present invention can be applied to any digitalcamera sensor (block 12) producing partial columns or entire columns ofcorrupted image data. In addition to a single column corruption, thepresent invention also addresses the problem of double columncorruption, in which two adjacent sensor columns produce corrupted data.The algorithm for replacing the corrupted image data can be implimentedin the digital signal processing block 22, although other arrangementsare possible. The present invention addresses column defects for a Bayerpattern RGB sensor, although it is understood that the method can beapplied to other filter combinations.

Referring to FIG. 2, when an entire column of data is corrupted, thereare only three convenient directions for interpolation: slash (+1 slope)(line 42), horizontal (line 44), and backslash (−1 slope) (line 46). Forgreen pixel repair in a single corrupted column, FIG. 2 shows thedirections and the known surrounding green values. In the case of adouble column corruption, the situation is similar but the problem ismore difficult because valid data is now further away. In FIG. 3 areshown the three directions used for green pixel repair when two adjacentcolumns of data are corrupted. Correspondingly, the three directionsare: slash (line 52), horizontal (line 54), and backslash (line 56).

Once the corrupted green values have been replaced, attention turns tothe corrupted red or blue values. These values are found byinterpolating the color differences (R−G) and (B−G). For a singlecorrupted column, FIG. 4 shows the three directions used for red, as anexample. Correspondingly, the three directions are: slash (line 62),horizontal (line 64), and backslash (line 66). Because of the spacing ofthe red and blue pixels, FIG. 5 (depicting a double column corruption)shows that color difference interpolation may be handled the same way asshown in FIG. 4. Correspondingly, the three directions are: slash (line72), horizontal (line 74), and backslash (line 76). Green values areshown in the shaded columns because the replaced green values are knownat the time of color difference interpolation.

Following the pattern of FIG. 2, the diagram below shows the known greenvalues in the case of a single column corruption. The corrupted columnis column 5 and the question marks “???” at position 55 (i.e. col 5, row5) locate the corrupted green value, G55, to be replaced. To illustratea specific case, column 5 is assumed to be a green/blue column, socolumns 4 and 6 are green/red columns.

col 3 4 5 6 7 row G33 R43 R63 G73 3 G44 G64 4 G35 R45 ??? R65 G75 5 G46G66 6 G37 R47 R67 G77 7

First, two temporary green values, g45 and g65, are computed as follows:

g45=(−R43+3*G44+2*R45+3*G46−R47+3)/6

g65=(−R63+3*G64+2*R65+3*G66−R67+3)/6

The values g45 and g65 are temporary and are NOT the values G45 and G65which appear later. Next, define some classifier values to assist indetermining which is the preferred interpolation direction for replacingthe corrupted green value. The directions are denoted as slash, horz,and back (“horz” for horizontal and “back” for backslash). Using “Abs”to denote the absolute value function, the classifiers as defined asfollows:

Clas(Slash)=Abs(G35−G44)+Abs(G46−G64)+Abs(G66−G75)+Abs(G37−G46)+Abs(G64−G73)

Clas(Horz)=Abs(G44−G64)+Abs(g45−g65)+Abs(G46−G66)+Abs(G35−g45)+Abs(g65−G75)

Clas(Back)=Abs(G35−G46)+Abs(G44−G66)+Abs(G64−G75)+Abs(G33−G44)+Abs(G66−G77)

and the auxiliary value:

Aux(Horz)=Abs(G44−G46)+Abs(2*R45−R43−R47)+Abs(G64−G66)+Abs(2*R65−R63−R67)

Accordingly, the following predictor values are defined:

Pred(Slash)=(4*(G46+G64)−(G37+G73)+3)/6

Pred(Horz_Hard)=(G35+G75)/2

 Pred(Horz_Soft)=(4*(g45+g65)−(G35+G75)+3)/6

Pred(Back)=(4*(G44+G66)−(G33+G77)+3)/6

Pred(Vert)=(g45+g65)/2

As will become clear hereinafter, computed classifiers based on adjacentnon-corrupted pixel data identify those cases in which there is ahorizontal edge or a diagonal edge feature which passes through thedefective column. Thereafter, the process adaptively replaces the datain the corrupted image pixels by selecting an algorithm which correpondsto the edge feature identified by the classifier and using the validdata in the neighboring non-corrupted pixels of the selected edgefeature.

The logic for utilizing the classifier values and selecting the properpredictor value, for example where corrupted green pixel G55 needs to bereplaced.

IF Clas(Horz) < Min( Clas(Slash), Clas(Back)) THEN IF Threshold_1 <Aux(Horz) THEN IF Threshold_2 < Aux(Horz) THEN set G55 = Pred(Horz_Hard)ELSE set G55 = Pred(Horz_Soft) ENDIF ELSE set G55 = Pred(Vert) ENDIFELSE IF Clas(Slash) < Clas(Back) THEN set G55 = Pred(Slash) ELSE set G55= Pred(Back) ENDIF ENDIF

Typical values for Threshold_(—)1 and Threshold_(—)1 for an 8-bit imageare 80 and 100 respectively.

Using the above algorithm the corrupted value for pixel G55 is nowreplaced. In a similar manner, the remaining corrupted green pixels arealso replaced. Having replaced the corrupted green values, the corruptedred and blue values are now considered. To illustrate a specific case,the following account is done for replacing a corrupted red value. Thevery same action would be taken for blue. The diagram below follows thepattern shown in FIG. 4. As before, the pixel of interest is located inthe 55 position, containing the question marks “???”. Because thecorrupted green replacement has already been done, there are now validgreen value defined above and below this position.

col 3 4 5 6 7 row R33 G43 G63 R73 3 G34 B44 G54 B64 G74 4 R35 G45 ???G65 R75 5 G36 B46 G56 B66 G76 6 R37 G47 G67 R77 7

The process starts by summing the four central green values:

Green(Ctr)=G54+G45+G56+G65

Next, three more green values are computed as follows:

Green(Slash)=(Green(Ctr)−(G36+G47+G74+G63))/2

Green(Horz)=(Green(Ctr)−(G34+G36+G76+G74))/2

Green(Back)=(Green(Ctr)−(G43+G34+G67+G76))/2

These three green values are used in two ways. Their absolute values areused as classifiers, and they are also used as corrector terms in thecorresponding predictor equations which follow:

Pred(Slash)=(R37+R73+Green(Slash))/2

Pred(Horz)=(R35+R75+Green(Horz))/2

Pred(Back)=(R33+R77+Green(Back))/2

The logic for finding the restored red value (R55) is as follows:

IF Abs(Green(Horz)) < Min( Abs( Green(Slash) ), Abs( Green(Back))) THENset R55 = Pred(Horz) ELSE IF Abs( Green(Slash)) < Abs( Green(Back)) THENset R55 = Pred(Slash) ELSE set R55 = Pred(Back) ENDIF ENDIF

This completes the description of the algorithm for a single corruptedcolumn. Now the algorithm for handling a double column corruption willbe discussed. These two algorithms (for single and double columndefects) may be applied as many times as there are single and doublecolumn corruptions in an image, and they may be applied in any order.The only requirement is that two valid columns must appear on each sideof the corrupted column or columns. For example, these correspond tocolumns 3, 4, 6, and 7 in the pixel neighborhood shown above.

Following the pattern of FIG. 3, the diagram below shows the valid greenvalues in the case of a double column corruption. The corrupted columnsare columns 5 and 6 and the question marks “???” at position 55 locatethe corrupted green value to be restored. As before, replacing thecorrupted green values is the first order of business.

col 3 4 5 6 7 8 row G73 3 G44 — G84 4 G35 ??? G75 5 G46 — G86 6 G77 7

Although the pixel of interest has been chosen from the left handcorrupted column, the reasoning and the equations that follow may beapplied to the right hand column as well. One would simply draw themirror image of the above diagram so that columns 4 and 5 become thecorrupted ones.

First the following classifier values are computed:

Clas(Slash)=Abs(G35−G44)+Abs(G46−G73)+Abs(G77−G86)

Clas(Horz)=Abs(G44−G84)+Abs(G35−G75)+Abs(G46−G86)

Clas(Back)=Abs(G35−G46)+Abs(G44−G77)+Abs(G73−G84)

and the auxiliary value is computed:

Aux(Horz)=Abs(G44+G84−G46−G86)

Next, the following predictor values are computed:

Pred(Slash)=(2*G46+G73+1)/3

Pred(Horz)=(G35+G75)/2

Pred(Back)=(2*G44+G77+1)/3

Pred(Vert)=(Pred(Slash)+Pred(Back))/2

The logic for utilizing the classifier values and selecting the properpredictor value is similar to the logic used in the previous case of asingle corrupted column.

IF Clas(Horz) < Min( Clas(Slash), Clas(Back)) THEN IF Threshold_3 <Aux(Horz) THEN set G55 = Pred(Horz) ELSE set G55 = Pred(Vert) ENDIF ELSEIF Clas(Slash) < Clas(Back) THEN set G55 = Pred(Slash) ELSE set G55 =Pred(Back) ENDIF

In this case, a typical value for Threshold_(—)3 for an 8-bit image is24.

Having replaced corrupted green values, the corrupted red and bluevalues are now considered. As before, to illustrate a specific case, thefollowing account is done for replacing a corrupted red value. The verysame action would be taken for blue. The diagram below follows thepattern shown in FIG. 5. As before, the pixel of interest is located inthe 55 position, containing the question marks “???”. In addition, allthe surrounding green value are known to be valid. This diagram for thedouble column case is nearly identical to the corresponding diagram forthe single column case. The only difference is that the blue values ofcolumn 6 are corrupted because columns 5 and 6 are the two corruptedcolumns in this scenario and the blue values haven't been replaced yet.However, the blue values played no part in the single column algorithm'sreplacment of the corrupted red pixels. Consequently, the single columnalgorithm for red replacement can be used in the double column case withno modification.

col 3 4 5 6 7 row R33 G43 G63 R73 3 G34 B44 G54 G74 4 R35 G45 ??? G65R75 5 G36 B46 G56 G76 6 R37 G47 G67 R77 7

Since the replacement of corrupted red and blue pixels is the final stepin column defect concealment, the description of the double columnalgorithm is now complete.

The invention has been described in detail with particular reference tocertain preferred embodiments thereof, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention.

PARTS LIST 2 input section 4 interpolating and recording section 10exposure section 12 image sensor 13 color filter array 14 output diode16 A/D converter 18 image buffer 20 control processor 22 digital signalprocessor 24 removable memory card 26 connector 28 processing buffer 30operational panel

What is claimed is:
 1. A method for correcting for defects in a digitalimage taken by an image sensor when there are pre-existing defects intwo pixels in adjacent columns of the image sensor which causes twoadjacent lines of pixels in the digital image to have corrupted data,comprising the steps of: (a) providing a defect map which identifies theposition of the defective pixels and specifies the two adjacent lines ofpixels which during readout will be caused to have corrupted data; (b)capturing the digital image in the image sensor and reading out suchdigital image to provide the digital image with the two adjacent linesof pixels in the digital image having corrupted data; (c) computingclassifiers based on adjacent non-corrupted pixel data which indicatethat there is a horizontal edge or a diagonal edge feature which passesthrough the defective lines of pixels; and (d) adaptively replacing thedata in the corrupted image pixels by selecting an algorithm whichcorreponds to the edge feature identified by the classifier and usingthe valid data in the neighboring non-corrupted pixels of the selectededge feature.
 2. A method for correcting for defects in a digital imagetaken by a full frame CCD when there is a pre-existing defect in atleast one pixel in the full frame CCD which causes two adjacent lines ofpixels in the digital image to have corrupted data, comprising the stepsof: (a) providing a defect map which identifies the position of thedefective pixel in the full frame CCD and specifies the two adjacentlines of pixels which during readout will be caused to have corrupteddata; (b) capturing the digital image in the full frame CCD and readingout such digital image to provide the digital image with the twoadjacent lines of pixels having corrupted data; (c) computingclassifiers based on adjacent non-corrupted pixel data which indicatethat there is a horizontal edge or a diagonal edge feature which passesthrough the defective lines of pixels; and (d) adaptively replacing thedata in the corrupted image pixels by selecting an algorithm whichcorreponds to the edge feature identified by the classifier and usingthe valid data in the neighboring non-corrupted pixels of the selectededge feature.
 3. A method for correcting for defects in a digital imagetaken by a full frame color CCD having a plurality of different coloredpixels wherein there is a pre-existing defect in at least two colorpixels in the full frame color CCD which causes two adjacent lines ofpixels of different colors in the digital image to have corrupted data,comprising the steps of: (a) providing a defect map which identifies theposition of the defective pixel in the full frame color CCD andspecifies the two adjacent lines of pixels which during readout will becaused to have corrupted data; (b) capturing the digital image in thefull frame color CCD and reading out such digital image to provide thecolored digital image with the two adjacent lines of pixels havingcorrupted data; (c) computing classifiers based on adjacentnon-corrupted pixel data which indicate that there is a horizontal edgeor a diagonal edge feature which passes through the defective lines ofpixels; and (d) adaptively replacing the data in the corrupted imagepixels by selecting an algorithm which correponds to the edge featureidentified by the classifier and using the valid data in the neighboringnon-corrupted pixels of the selected edge feature.
 4. The method ofclaim 3 wherein the full frame color CCD is provided in a single sensordigital camera which produces single color values for each color pixelin the digital image and includes the step of: (e) interpolating colorpixels in the digital image to provide for a plurality of color valuesfor each color pixel in the digital image and wherein step (d) isprovided before or after the interpolation step.
 5. The method of claim3 wherein the replacing step includes providing a plurality ofclassifiers based upon neighboring non-corrupted color pixels andselecting at least one of the classifiers to identify an appropriateformula for operating on neighboring non-corrupted color pixels toreplace the values of the corrupted color pixels in the captured image.6. A method for correcting for defects in a digital image taken by afull frame color CCD having a plurality of different colored pixelswherein there is a pre-existing defect in at least two color pixels inthe full frame color CCD which causes two adjacent lines of pixels ofdifferent colors in the digital image to have adjacent corrupted datathat are two pixels wide and at least a portion of one of the lines tohave corrupted data which are one pixel wide, comprising the steps of:(a) providing a defect map which identifies the position of thedefective pixel in the full frame color CCD and specifies the twoadjacent lines of pixels which during readout will be caused to havecorrupted data; (b) capturing the digital image in the full frame colorCCD and reading out such digital image to provide the colored digitalimage with the two adjacent lines of pixels having corrupted data; (c)computing classifiers based on adjacent non-corrupted pixel data whichindicate that there is a horizontal edge or a diagonal edge featurewhich passes through the defective lines of pixels for the two pixelswide corrupted data or the one pixel wide corrupted data; and (d)adaptively replacing the data in the corrupted image pixels by selectingan algorithm which correponds to the edge feature identified by theclassifier and using the valid data in the neighboring non-corruptedpixels of the selected edge feature.